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Old Missions, New Discoveries: NASA’s Data Archives Accelerate Science


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Old Missions, New Discoveries: NASA’s Data Archives Accelerate Science

 This solar system montage of the nine planets and four large moons of Jupiter in our solar system are set against a false-color view of the Rosette Nebula.
This montage of images taken by the Voyager spacecraft of the planets and four of Jupiter’s moons is set against a false-color picture of the Rosette Nebula with Earth’s moon in the foreground. Archival data from the Voyager missions continue to produce new scientific discoveries.
NASA/JPL/ASU

Every NASA mission represents a leap into the unknown, collecting data that pushes the boundaries of human understanding. But the story doesn’t end when the mission concludes. The data carefully preserved in NASA’s archives often finds new purpose decades later, unlocking discoveries that continue to benefit science, technology, and society.

“NASA’s science data is one of our most valuable legacies,” said Kevin Murphy, NASA’s chief science data officer at NASA Headquarters in Washington. “It carries the stories of our missions, the insights of our discoveries, and the potential for future breakthroughs.”

NASA’s science data is one of our most valuable legacies.

Kevin Murphy

Kevin Murphy

Chief Science Data Officer, NASA’s Science Mission Directorate

NASA’s Science Mission Directorate manages an immense amount of data, spanning astrophysics, biological and physical sciences, Earth science, heliophysics, and planetary science. Currently, NASA’s science data holdings exceed 100 petabytes—enough to store 20 billion photos from the average modern smartphone. This volume is expected to grow significantly with new missions.

This vast amount of data enables new discoveries, connecting scientific observations together in meaningful ways. Over 50% of scientific publications rely on archived data, which NASA provides to millions of commercial, government, and scientific users.

Image snippets showing Earth, the Carina nebula, Jupiter, the surface of the Sun, and a cell.
NASA’s five science divisions — Astrophysics, Biological and Physical Sciences, Earth Science, Heliophysics, and Planetary Science — store petabytes’ worth of data in their archives that enable scientists to continually make discoveries.
NASA

Managing and stewarding such massive volumes of information requires careful planning, robust infrastructure, and innovative strategies to ensure the data is accessible, secure, and sustainable. Continued support for data storage and cutting-edge technology is key to ensuring future generations of researchers can continue to explore using science data from NASA missions. 

Modern technology, such as image processing and artificial intelligence, helps unlock new insights from previous observations. For example, in 1986, NASA’s Voyager 2 spacecraft conducted a historic flyby of Uranus, capturing detailed data on the planet and its environment. Decades later, in the early 2000s, scientists used advanced image processing techniques on this archival data to discover two small moons, Perdita and Cupid, which had gone unnoticed during the initial analysis.

In 2024, researchers revisited this 38-year-old archival data and identified a critical solar wind event that compressed Uranus’s magnetosphere just before the Voyager 2 flyby. This rare event, happening only about four percent of the time, provided unique insights into Uranus’s magnetic field and its interaction with space weather.

The energized particles around Uranus defied scientist's understanding of how magnetic fields work to trap particle radiation.
The first panel of this artist’s concept depicts how Uranus’s magnetosphere (its protective bubble) was behaving before Voyager 2’s flyby. The second panel shows that an unusual kind of solar weather was happening at the same time as the spacecraft’s flyby, giving scientists a skewed view of Uranus’s magnetosphere. The work enabled by archival Voyager data contributes to scientists’ understanding of this enigmatic planet.
NASA/JPL-Caltech

NASA’s Lunar Reconnaissance Orbiter (LRO), launched in 2009, continues to provide data that reshapes our understanding of the Moon. In 2018, scientists analyzing the LRO’s archival data confirmed the presence of water ice in permanently shadowed regions at the Moon’s poles. 

In 2024, new studies out of NASA’s Goddard Space Flight Center in Greenbelt, Maryland, showed widespread evidence of water ice within the permanently shadowed regions outside the lunar South Pole, further aiding lunar mission planners. This discovery not only holds implications for lunar exploration but also demonstrates how existing data can yield groundbreaking insights.

Artist rendering of the Lunar Reconnaissance Orbiter LRO, above the moon.
Artist rendering of the Lunar Reconnaissance Orbiter (LRO) above the Moon. LRO carries seven instruments that make comprehensive remote sensing observations of the Moon and measurements of the lunar radiation environment. Archival data from LRO continues to help scientists make discoveries about the Moon.
NASA/GSFC

NASA’s data archives uncover the secrets of our own planet as well as others. In 2024, archaeologists published a study revealing a “lost” Mayan city in Campeche, Mexico that was previously unknown to the scientific community. The researchers identified the city in archival airborne Earth science data, including a 2013 dataset from NASA Goddard’s LiDAR Hyperspectral & Thermal Imager (G-LiHT) mission.

The Harmonized Landsat and Sentinel-2 (HLS) project provides frequent high-resolution observations of Earth’s surface. Data from HLS has been instrumental in tracking urban growth over time. By analyzing changes in land cover, researchers have used HLS to monitor the expansion of cities and infrastructure development. For example, in rapidly growing metropolitan areas, HLS data has revealed patterns of urban sprawl, helping planners analyze past trends to predict future metropolitan expansion.


Landsat5Ontario1985.jpg
Landsat5Ontario2010.jpg
1985
2010

This natural color Landsat 5 image shows the city of Ontario, California, from above in the year 1985. Vegetation is green and brown and urban structures are bright white and gray. There is a large dry riverbed in the northeast corner that is also bright white, but its nonlinear appearance sets it apart visually.
NASA’s Goddard Space Flight Center

This natural color Landsat 5 image shows the city of Ontario, California, from above in the year 2010. The airport, found in the southwest portion of the image, added a number of runways since 1985, and large warehousing structures now dominate the once rural areas surrounding the airport. Vegetation is green and brown and urban structures are bright white and gray. There is a large dry riverbed in the northeast corner that is also bright white, but its nonlinear appearance sets it apart visually.
NASA’s Goddard Space Flight Center

This natural color Landsat 5 image shows the city of Ontario, California, from above in the year 1985. Vegetation is green and brown and urban structures are bright white and gray. There is a large dry riverbed in the northeast corner that is also bright white, but its nonlinear appearance sets it apart visually.
NASA’s Goddard Space Flight Center
This natural color Landsat 5 image shows the city of Ontario, California, from above in the year 2010. The airport, found in the southwest portion of the image, added a number of runways since 1985, and large warehousing structures now dominate the once rural areas surrounding the airport. Vegetation is green and brown and urban structures are bright white and gray. There is a large dry riverbed in the northeast corner that is also bright white, but its nonlinear appearance sets it apart visually.
NASA’s Goddard Space Flight Center

1985

2010

Before and After

Urban Growth in Ontario, California

1985-2010




Thirty-five miles due east of downtown Los Angeles lies the city of Ontario, California. These natural color Landsat 5 images show the massive growth of the city between 1985 and 2010. The airport, found in the southwest portion of the images, added a number of runways, and large warehousing structures now dominate the once rural areas surrounding the airport. In these images, vegetation is green and brown, while urban structures are bright white and gray. A large dry riverbed in the northeast corner is also bright white, but its nonlinear appearance sets it apart visually. Researchers use archival data from Landsat and other satellites to track the growth of cities like Ontario, CA over time.

These discoveries represent only a fraction of what’s possible. NASA is investing in new technologies to harness the full potential of its data archives, including artificial intelligence (AI) foundation models—open-source AI tools designed to extract new findings from existing science data.

“Our vision is to develop at least one AI model for each NASA scientific discipline, turning decades of legacy data into a treasure trove of discovery,” said Murphy. “By embedding NASA expertise into these tools, we ensure that our scientific data continues to drive innovation across science, industry, and society for generations to come.”

Developed under a collaboration between NASA’s Office of the Chief Science Data Officer, IBM, and universities, these AI models are scientifically validated and adaptable to new datasets, making them invaluable for researchers and industries alike.

“It’s like having a virtual assistant that leverages decades of NASA’s knowledge to make smarter, quicker decisions,” said Murphy.

GSFC_20171208_Archive_e001431~large.jpg?
On June 22, 2013, the Operational Land Imager (OLI) on Landsat 8 captured this false-color image of the East Peak fire burning in southern Colorado near Trinidad. Burned areas appear dark red, while actively burning areas look orange. Dark green areas are forests; light green areas are grasslands. Data from Landsat 8 were used to train the Prithvi artificial intelligence model, which can help detect burn scars.
NASA Earth Observatory

The team’s Earth science foundation models—the Prithvi Geospatial model and Prithvi Weather model—analyze vast datasets to monitor Earth’s changing landscape, track weather patterns, and support critical decision-making processes.

Building on this success, the team is now developing a foundation model for heliophysics. This model will unlock new insights about the dynamics of solar activity and space weather, which can affect satellite operations, communication systems, and even power grids on Earth. Additionally, a model designed for the Moon is in progress, aiming to enhance our understanding of lunar resources and environments.

This investment in AI not only shortens the “data-to-discovery” timeline but also ensures that NASA’s data archives continue to drive innovation. From uncovering new planets to informing future exploration and supporting industries on Earth, the possibilities are boundless.

By maintaining extensive archives and embracing cutting-edge technologies, the agency ensures that the data collected today will continue to inspire and inform discoveries far into the future. In doing so, NASA’s legacy science data truly remains the gift that keeps on giving.

By Amanda Moon Adams
Communications Lead for the Office of the Chief Science Data Officer

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      Illustrations of Solar Dynamics Observatory solar imagery used for training Surya: Solar coronal ultraviolet and extreme ultraviolet images from the Atmospheric Imaging Assembly (AIA) and solar surface velocity and magnetic field maps from the Helioseismic and Magnetic Imager (HMI). NASA/SDO The model’s success builds directly on the Solar Dynamics Observatory’s long-term database. Launched in 2010, NASA’s Solar Dynamics Observatory has provided an unbroken, high-resolution record of the Sun for nearly 15 years through capturing images every 12 seconds in multiple wavelengths, plus precise magnetic field measurements. This stable, well-calibrated dataset, spanning an entire solar cycle, is uniquely suited for training AI models like Surya, enabling them to detect subtle patterns in solar behavior that shorter datasets would miss.
      Surya’s strength lies in its foundation model architecture, which learns directly from raw solar data. Unlike traditional AI systems that require extensive labeling, Surya can adapt quickly to new tasks and applications. Applications include tracking active regions, forecasting flare activity, predicting solar wind speed, and integrating data from other observatories including the joint NASA-ESA Solar and Heliospheric Observatory mission and NASA’s Parker Solar Probe.
      “We are advancing data-driven science by embedding NASA’s deep scientific expertise into cutting-edge AI models,” said Kevin Murphy, chief science data officer at NASA Headquarters in Washington. “By developing a foundation model trained on NASA’s heliophysics data, we’re making it easier to analyze the complexities of the Sun’s behavior with unprecedented speed and precision. This model empowers broader understanding of how solar activity impacts critical systems and technologies that we all rely on here on Earth.”
      These images compare the ground-truth data (right) with model output (center) for solar flares, which are the events behind most space weather. Surya’s prediction is very close to what happened in reality (right). These preliminary results suggest that Surya has learned enough solar physics to predict the structure and evolution of a solar flare by looking at its beginning phase. NASA/SDO/ODSI IMPACT AI Team Solar storms pose significant risks to our technology-dependent society. Powerful solar events energize Earth’s ionosphere, resulting in substantial GPS errors or complete signal loss to satellite communications. They also pose risks to power grids, as geomagnetically induced currents from coronal mass ejections can overload transformers and trigger widespread outages.
      In commercial aviation, solar flares can disrupt radio communications and navigation systems while exposing high-altitude flights to increased radiation. The stakes are even higher for human spaceflight. Astronauts bound for the Moon or Mars may need to depend on precise predictions to shelter from intense radiation during solar particle events.
      The Sun’s influence extends to the growing number of low Earth orbit satellites, including those that deliver global high-speed internet. As solar activity intensifies, it heats Earth’s upper atmosphere, increasing drag that slows satellites, pulls them from orbit, and causes premature reentry. Satellite operators often struggle to forecast where and when solar flares might affect these satellites.
      The “ground truth” solar activity is shown on the top row. The bottom row shows solar activity predicted by Surya. NASA/SDO/ODSI IMPACT AI Team “Our society is built on technologies that are highly susceptible to space weather,” said Joseph Westlake, Heliophysics Division director at NASA Headquarters. “Just as we use meteorology to forecast Earth’s weather, space weather forecasts predict the conditions and events in the space environment that can affect Earth and our technologies. Applying AI to data from our heliophysics missions is a vital step in increasing our space weather defense to protect astronauts and spacecraft, power grids and GPS, and many other systems that power our modern world.”
      While Surya is designed to study the Sun, its architecture and methodology are adaptable across scientific domains. From planetary science to Earth observation, the project lays the foundational infrastructure for similar AI efforts in diverse domains.
      Surya is part of a broader NASA push to develop open-access, AI-powered science tools. Both the model and training datasets are freely available online to researchers, educators, and students worldwide, lowering barriers to participation and sparking new discoveries.
      The process for creating Surya. Foundation models enhance the utility of NASA’s Solar Dynamics Observatory datasets and create a base for building new applications. NASA/ODSI IMPACT AI Team Surya’s training was supported in part by the National Artificial Intelligence Research Resource (NAIRR) Pilot, a National Science Foundation (NSF)-led initiative that provides researchers with access to advanced computing, datasets, and AI tools. The NAIRR Pilot brings together federal and industry resources, such as computing power from NVIDIA, to expand access to the infrastructure needed for cutting-edge AI research.
      “This project shows how the NAIRR Pilot is uniting federal and industry AI resources to accelerate scientific breakthroughs,” said Katie Antypas, director of NSF’s Office of Advanced Cyberinfrastructure. “With support from NVIDIA and NSF, we’re not only enabling today’s research, we’re laying the groundwork for a national AI network to drive tomorrow’s discoveries.”
      Surya is part of a larger effort championed and supported by NASA’s Office of the Chief Science Data Officer and Heliophysics Division, the NSF , and partnering universities to advance NASA’s scientific missions through innovative data science and AI models. Surya’s AI architecture was jointly developed by the Interagency Implementation and Advanced Concepts Team (IMPACT) under the Office of Data Science and Informatics  at NASA’s Marshall Space Flight Center in Huntsville, Alabama; IBM; and a collaborative science team.
      The science team, assembled by NASA Headquarters, consisted of experts from the Southwest Research Institute in San Antonio, Texas; the University of Alabama in Huntsville in Huntsville, Alabama; the University of Colorado Boulder in Boulder, Colorado; Georgia State University in Atlanta, Georgia; Princeton University in Princeton, New Jersey; NASA’s SMD’s Heliophysics Division; NASA’s Goddard Space Flight Center in Greenbelt, Maryland; NASA’s Jet Propulsion Laboratory in Pasadena, California; and the SETI Institute in Mountain View, California.
      For a behind-the-scenes dive into Surya’s architecture, industry and academic collaborations, challenges behind developing the model, read the blog post on NASA’s Science Data Portal:
      https://science.data.nasa.gov/features-events/inside-surya-solar-ai-model
      For more information about NASA’s strategy of developing foundation models for science, visit:
      https://science.nasa.gov/artificial-intelligence-science
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      Last Updated Aug 20, 2025 Related Terms
      Science & Research Artificial Intelligence (AI) Heliophysics Solar Dynamics Observatory (SDO) The Sun The Sun & Solar Physics Explore More
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